Lead Software Engineer - Python, Databricks, AWS

JPMorgan Chase & Co.•Plano, TX

20h

About The Position

As a Lead Software Engineer at JPMorgan Chase within the Corporate Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Proven experience in data management, ETL/ELT pipeline development, and large-scale data processing.
Proficiency in SQL, Python, and PySpark, with experience in query optimization and performance tuning.
Hands-on experience with data lake platforms (Databricks, Apache Spark, or similar).
Experience with AWS cloud services (S3, ECS, SNS/SQS, Lambda, etc.).
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Strong understanding of data quality, security, and lineage best practices.
Experience with cloud-based data warehouse migration and modernization.
Proficient in CI/CD, continuous delivery methods (Jules/Jenkins, Spinnaker, Sonar), the full Software Development Life Cycle, and Agile methodologies.
Excellent problem-solving, troubleshooting, and analytical skills with ability to investigate data issues, identify root causes, and implement solutions.

Nice To Haves

Knowledge of data pipeline tools such as PySpark, Snowflake, or Databricks.
Experience with data orchestration tools (Airflow, Step Functions, etc.).
Databricks or AWS certifications.
In-depth knowledge of the financial services industry and their IT systems.

Responsibilities

Execute creative, data-driven software solutions, including design, development, and technical troubleshooting, with the ability to think beyond routine approaches to solve technical problems.
Design, develop, and maintain scalable data pipelines and processing workflows using Python, PySpark, SQL, and Databricks on AWS, processing and transforming large-scale financial datasets for analytics and reporting.
Develop fact and dimension data models for reporting and analytics.
Write secure, high-quality production code, and review and debug code written by others.
Ensure data quality, consistency, security, and lineage throughout all stages of data processing and transformation, implementing monitoring and alerting mechanisms to maintain pipeline reliability.
Support data migration and modernization initiatives, transitioning legacy systems to cloud-based data warehouses.
Collaborate with business stakeholders to develop data management strategies, transforming data into insights that drive strategic decisions.
Document data flows, logic, and transformation rules to maintain transparency and facilitate knowledge sharing.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Utilize AI tools to accelerate development and testing of data pipelines (e.g. GitHub CoPilot, Claude Code).